The Leadership Behaviors That Drive AI Adoption

0:00Hi, everybody. A quick programming note for today's episode. We're doing something a little different, but no less timely. We're sharing an episode that originally aired on Better, the podcast from Great Place to Work. I stepped into the co-host chair alongside Roula Amire for a conversation with WWT Chief Information Officer Kevin Dana about why some organizations embrace AI while others struggle to gain traction. We get into trust, leadership, experimentation, and the very human side of change because the technology is only part of the equation. It's practical, candid, and right in the AIPG wheelhouse.

0:35Hope you enjoy it. Welcome to Better by Great Place to Work, the global authority on workplace culture. I'm your host, Rula Amiri, Content Director at Great Place to Work.

0:51This episode is a first. For the first time, I'm joined by a co-host, Brian Felt, host of the AI Proving Ground podcast from Worldwide Technology, sponsor of this season of our podcast, joins me today. And our guest is Kevin Dana, SVP and Chief Information Officer at Worldwide. What I loved about this episode is how practical it is. Kevin talks about what has and hasn't worked at Worldwide when it comes to AI adoption with real-life lessons learned. We talked about open mic nights and AI champions, experimentation, and why failure is often a necessary part of innovation. I know you're going to walk away with a lot of ideas you can put to use right away.

1:38So enjoy my conversation with Kevin and Brian. Kevin, welcome to the podcast. Thank you. Good to be here. It's great to have you on. I am particularly excited because I'm joined by a co-host for the first time, Brian Felt. Brian, you are host of the AI Proving Ground podcast presented by Worldwide Technology. Thanks for being here. Absolutely. Thanks for having me. I'm looking forward to co-hosting. Haven't done it before, but it's going to be an adventure. I haven't done it either. Never had a co-host, so we'll learn together. I recently learned you have a background in journalism. That's right.

2:14As do I. So, Kevin, fair warning, you might have some extra follow-up questions. Sounds like most of you will do the talking since I have a technology background. Here comes the grilling hard questions. Sounds good. We'll keep it painless. I'll promise. Sure. Kevin, as the Chief Information Officer at Worldwide, you have a front row seat to what's happening in tech today. We see study after study that shows adoption levels are low and executives are frustrated because their people aren't using AI as much as they like or fast enough, etc. From where you sit, what are you hearing from leaders? It is pretty similar to that.

2:51I think there's a lot of noise out in the world, particularly if you are in any places where AI companies are espousing what they're able to do. and then that creates some confusion on what the tools can actually do so that creates a level of impatience and expectation and then with that as as employees are trying to use the tools whether in their personal life or at work there's varying degrees of success with those things and so there's kind of a tension that exists between those two those two expectations that we are as you said at the front row seat or actually on stage trying to figure out how do you bring those together and actually achieve solid business outcomes through the use of this new technology.

3:34Yeah. So just as you think about that, I mean, with the enterprise perspective here, what have you learned in terms of how much it's about a tools discussion versus a change management and leadership perspective? I would say it's really intertwined in many regards. It's more intertwined than anything else. So there's a huge demand for the tools. And as we just said, there's an expectation that the tools are going to be able to do things. But then there's the reality that sets in of how do you enable people to actually get the repeatable types of results out of them? If they're not repeatable, it's almost kind of the hot stove effect.

4:13Like, I tried it. I'm not going to do that again. But you see week on week that these things are advancing and what may have not worked last week will work this week. Now, whether it's repeatable becomes the next question. But there's actually an aspect of people building trust in the tools that they're being expected to use that we need to overcome through change management. And with that, we need to also say, hey, it's okay in these scenarios to experiment, try things out, and learn through those things yourself, as opposed to waiting to have something that comes out that says, use this tool for this thing and expect this outcome.

4:48So there's an aspect here that's a little bit more challenging of getting people more comfortable with trying things out and giving time and space for them to do it as well. And that's more likely to happen in a high-trust environment. So they'll experiment if they feel safe to try and learn new things, which we'll get into. So let's dig into that. So to your point, our research shows that it's not the tools that slow adoption. It's the leaders. Adoption goes up by more than 100 % if leaders talk about AI, use it themselves, not delegate, and connect it to people's real work and careers. So those are the three drivers that we've seen, according to our research, that really drive AI adoption.

5:37Does any of that surprise you? Not at all, no. I mean, I'd say our experience has been, like you said, from the CEO, Jim, through the executive team on down, there's been early discussions around where we see AI fitting into worldwide and what we expect worldwide to be as a company as we go through this transformation. and even just in town halls and in corporate updates even more opening of hey we don't have all the answers but this is where we want to go and we want to go with you on this journey so that that that aspect is there as well as the lead by example which i think we probably will want to dig into as well it's not just go do that and then get back to me it's like here's how i'm seeing it being used here's how i'd like to see you try to make it used but but it needs to also have that trust and that feedback loop of listening to, we tried this, it didn't work.

6:34We want to try something else. Not we tried this, we're done, but keep it as a feedback loop of experimentation and innovation. Let's unpack a little bit about, you mentioned our CEO, Jim Cavanaugh at Worldwide Technology. You know, Ruel, you're talking about leaders that talk about AI, help drive AI. I can't think of anybody that's talking more about AI than Jim is. Take us back to maybe the moment where, you know, ChatGPT exploded. It's late 2022 or maybe early 2023. And Jim's telling the executive leadership and yourself, we want to be an AI first company. What do you remember about that time?

7:11How did you start to implement that? Where did frictions arise? How did you start building teams around it? Just what was a little bit of that game plan? That was a frenetic. I was trying to forget all that, Brian, but no, it was frenetic. And part of that is like, as a technologist, you want to understand what this technology is and what you would be bringing into the environment. Because as an enterprise, you still have aspects of compliance and privacy and, you know, all of those types of things that you still have to make sure something is fit for purpose. And what's been interesting with AI is how quickly the progression has been.

7:46So when it first came out in 2022 and late 2022 and 23, it really was almost like a crystal ball thing. Like you ask a question, it gets something back and everybody's amazed that it provides coherent results. But you could not steer, you could not control what that was. So we went through these different phases of retrieval augmented generation. So, you know, being able to now control and feed corporate information and get corporate answers to now where we're at with the agentic age and tools. All of those things have been a journey. And with that would be, again, that feedback loop of what are we working on and how are we able to get results on that?

8:22what's ready for prime time, something that we can give to employees, what's not. And we just continue on that kind of iterative approach. And the frequency that we have for that at Worldwide is weekly. So as part of Jim's executive leadership meeting every Monday morning, there's an hour devoted to what are we doing with AI in each of the different business units. And those are led by the business leaders, back to the original point of how are we applying this, as opposed to IT coming and saying, you know, pushing rope uphill, like, here, use AI for this. So that changes the game and the nature of this.

8:55So we're not trying to catch up to what's being set out on X. It's how are we actually achieving business outcomes using this and being an AI first company? Yeah. How did that intense focus from Jim early on, even until today, how did that intense focus help permeate through the rest of the organization that helped others, you know, other managers within the organization also talk about AI and try to drive AI at a time in 2023 where, like you said, maybe the AI responses weren't as trustworthy as they are today. I think that the thing that that afforded was it's okay to dig into this and back to trust.

9:34It's okay for there to be mistakes. So early on, one of the projects that we picked to work on was a request for proposal tool. and we started to build the the tool and and it was early days and the early results were not landing it was the rfp team was saying you know we're not getting good results i would rather do this the old the old way than the new way and lo and behold back to technology advancements a new model came out we plugged the model in and boom all of a sudden the whole thing started to work so in in that case it was i mean a good story that it was fortunate that a little bit of skating to the puck and the technology hit.

10:13But that then created also an expectation that we continue to make bets on some things that are probably the leading edge so that when those things catch up, we're there and ready to go with them. So that happening, but that happening through business leaders and seeking to identify where we could do business process transformation using AI is really, I think, the mix. And that then helps with adoption because they're seeing, the employees are seeing where it fits into and changes the way that they work as opposed to, here's a general purpose tool, figure out how to make that do something. So we see adoption jump up by 150 % when leaders themselves use AI and talk about how they're using AI.

10:57And I'm hearing Jim is devoting every Monday at the executive meetings an hour on it. How does that play out at World worldwide outside of those meetings? What are leaders and other managers expected to do? How are they held accountable? Do they have meetings where they're talking about how they're using it, what they learn, what didn't work? So kind of normalizing the conversation around, we're trying things, because I'm hearing you say, we don't have all the answers. We try, we have this feedback loop. So is that part of the conversation that's normalizing? We're learning here. This is a learning culture.

11:36It is. I mean, you hit right on the nail on the head. Learning culture is a key aspect for worldwide and that, you know, don't fear failure, lean into it. And in that regard, in that cycle, the other thing that we've talked about in this is kind of controlled chaos, which in enterprise doesn't sound that great, but we recognize that there's just, there's too much that we, if we were trying to clamp it down and say, we're going to control this, we're going to define things and we'll roll them out in a controlled manner. It limits the surface area of what we could discover. And so to getting to your question, each of the business unit leaders have started to create their own kind of cadence on things.

12:13Like I know a few of the business units have a 60 minute call once a month where they just have it as an open mic night almost of tell us what you've used AI for and how you've been successful for it. So it's not a top down, it is a grassroots up, but it meets with the top down expectation and communication that we're an AI first company. So then when we saw those things happening, we also said to create a level of control of the chaos, we'll create an AI competency center. So we wanted to say, don't leave it to everybody's devices of how you get trained on it, et cetera, but let's learn from what is happening there, curate some of those things, and then make that available to the rest of the organization as well.

12:57So there's almost like little labs across the business units that then we can feed up. And so the last part I would say with that is the AI Competency Center was, we don't want it to be an IT-led initiative. We want it to be IT-supported so that we know what tools, how to, you know, where there are problems, et cetera. But there were AI champions. So we asked each of the business unit leaders to name business people that would be AI champions because we wanted people that knew their business processes, where their pain points were, and had enough technical acumen to dig into the tools to start to figure those things out.

13:32And so at this point, we have 200 AI champions, and that's continuing to grow. In fact, we have next week a summit where 500 people are coming together, and a subset of those 200 over a day and a half are just going to demo what they've done to everyone that's in attendance, and then we're going to record that and then also make that available to the company. So it really is showing AI in action and doing it in a way that is business-oriented, business-led as opposed to technology-led. Those AI champions and the AI in action events, those are very key. So, Ruel, I do want to come back to that.

14:08But maybe to backtrack a little bit, I like how you've termed it open mic night. I've definitely been part of some of those conversations. And it's not just a what are we doing with AI. It's also working with each other, supporting each other to walk through the challenges we're encountering with AI, the changes that are happening to our day-to-day jobs, because this is massive change that's happening on an individual level. And so with that context, I wanted to bring up our integrated management and leadership curriculum, which is a company-wide framework on how we, you know, basically our culture.

14:41And so maybe to the extent you can, how has our culture and those leadership and our core values, how has that helped enable teams on like the individual contributor level to lean in and trust AI and know that, you know, we can surface challenges and things like that. Well, you start with trust and that is one of the, that's the top, the number one in our culture is trust and with that an aspect of transparency. So we've led with, this is what we're doing. And I'll just draw it to myself and in some ways, IT is a business unit as well. So I'd say, you I was looking at all of these things and trying to then say, what is that journey going to look like?

15:23Because right now, one of the most disrupted parts of jobs is in AI coding assistance and how that impacts IT in the way that we deliver. And so we're hearing noise. So we had an all-hands meeting. I published an IT technology strategy document, said this is where we're going. And it was an overall three-year journey or three-year horizon, but broke it up into years. I said, I just want everybody to know, like, we have really three options here. One is we can say top down, this is what's going to happen. We can bury our head in the sand and hope that this just blows over and it isn't a thing. Or we can go on the journey together because we're not necessarily going to be able to figure out all the detailed elements.

16:04And we need you to be engaged to make those things happen. And that, as it ties back to trust, and the other one is around embracing change, is meeting them there to say, be clear about that. And then after that session, it was a live session, a couple days later hearing some noise, like people weren't happy with what you said. They feel disrupted and whatever. So we had a listening session the next week. And I said, I've heard the feedback. I want to acknowledge it. and I just said you know there are cases like this where it's a broad-based disruption but even in my own career I can point to at least two distinct moments in my career where what I had achieved the expertise around or whatever just was like I know every day this is what I'm going to do you know the next day I was like that's done we're moving on and so having to do those and just there's an emotion to that they're like I lost what I had so I expressed that to them and I said, I get it.

17:05Take some time. Think through that. Go through the grieving process of it. Talk to your manager. Talk to me. But also understand this is a change and we don't have control of that change happening to us. What we have is the control of how we respond to that change. So that just kind of is how we've applied IML, at least as an anecdote in IT. But that is uniformly what we're doing across worldwide. Yeah. And that doesn't necessarily put the challenges, you know, it doesn't brush them under the rug. It doesn't make them go away, but it does help give a sense of teamwork, collaboration, that you're tackling this together, just, you know, lending itself further to that trust.

17:42Right. Embrace the brutal facts is another aspect of our culture. So, yeah. I am curious, what role do you see HR in being a true enabler of AI adoption? How proactive should they be knowing that this can't just be, you know, initiative or even an IT and business initiative. It has to take, you know, all teams pulling together. There's two off the cuff, but I'm sure there's more since it's not my function. But the first and foremost is kind of to extend what we said there is HR typically owns training and enablement. And so the aspects of things like annual required training or competency training.

18:18So partnering to enable those, that kind of training, once we've kind of settled in on what those are and getting those established in the system so that the business unit leaders can say to, particularly to those employees that aren't as far along or not as interested in experimenting, here's where you start to go to get your training so that you can get skilled up, so you can catch up. And so they can provide that kind of partnership there. The other part of it that I think will come more so as we start to do the step back and look at How do these processes change? That means that roles and responsibilities will change.

18:54And so that obviously creates a component of both an empathetic point of view around how do we handle the messaging? How do we handle changes? And, you know, particularly less so maybe in the U.S., but if you're a global company, how does that comply with the regulations or the laws around what somebody was hired for and if those things are changing? So there's a lot that we'll have to go into thinking about those things intentionally and doing it in a way that goes back to showing that we understand our employees. We want to build trust and we want to do that in a transparent and a logical way, not in a haphazard and chaotic way.

19:32For the HR listener who says this sounds idyllic. I mean, this sounds amazing. The garden, yes. What advice would you have for, because there's a discussion at companies on who's leading AI. Is it the tech team? Is it HR around messaging? Are they partnering together? So for the listener who doesn't have a great partnership with their tech team, with the equivalent of a U, of the chief, you know, technology officer, what would any tips or advice on how they can build a stronger, you know, partnership? It does come down to people at that point, I'd say. And so maybe I'll turn the question around and be like, if as an IT person, as the IT leader, first realized with AI, it's too big to be one person's show.

20:27It just is, it's insurmountable. It's something I've frankly grappled with when I'm transparent. I felt for a long time, it's like, I need to figure this out. but then as the weight continues to get heavier and heavier and the scope gets broader and broader you realize this is just a big thing so in some ways it's not just create that relationship with hr although to your question that would be where i would start but it would be in some of these it's engage with your hr leader and say these things here's what i'm seeing coming just be open around here's what i see coming these may not be right but i want to talk to you what do you see coming make it a two-way conversation in a trusted smaller group first and then through that say well what actions do you think we should take maybe we have listening sessions and gather feedback that way that creates more signal to the noise back to even our earlier part of that conversation how are we making sure that our leaders are listening how do how do the hr partners that are embedded with the business functions acting as counselors to create the best practices to make sure that those things are happening.

21:29And then with that, I'd expand that to aspects of an organization's marketing and communications, because if you're going to pull off a town hall or a summit or something like that, they're the ones that have the expertise in that. So I think sometimes we get lost in if it's about AI, well, I'll carry the torch and figure out how to do all these things when in the end, it's still a team sport and you want to bring in, you know, everybody under the big tent and make those events happen. And when And that happens with the level of expertise and excellence that each of those functions can provide with their own subject matter expertise.

22:03You're pulling off a much better event, if you will call that, or experience that is genuine because you're not just trying to say, here's AI, but here's a conversation that you're having about where AI is meeting your workforce and where you're trying to achieve outcomes. So you're talking about now bringing in not just your IT teams, you're talking about bringing in HR, marketing, comms. I'm sure the list gets longer and longer. That's a complex process in and of itself. How do you align all those different parts? And I'm sure you don't have just a magic answer here, but is it about driving towards shared outcomes?

22:41Is it about being transparent about what type of outcomes you want? Or is it with the state of AI moving so fast, is it just about, hey, we're going to have to learn all this together and we need to rely on people skills and trust to get through it together. It's more the latter. It is a little bit of seat of the pants, if I'm honest. But again, it goes back to if the messaging and the expectations are communicated top down and the executive leadership of those business units is bought in, then you're not having to pull teeth to make that part happen. Then it becomes a matter of what are the expected outcomes.

23:14I'd say on the IT side, the other thing that you do have to do is carve out dedicated capacity to focus on those aspects of, I'll call it R &D, around what are the things that are coming? What does that mean? But that needs to be engaged with those portfolios to also say, let's try experimenting with these things in your spaces as opposed to it being done in a silo. And then here's what's going to happen.

23:44AI is reshaping jobs, teams, and leadership faster than most organizations are ready for. From strategy to security to adoption, the AI Proving Ground podcast presented by Worldwide Technology is a grounded look at what responsible AI leadership really requires in this era of rapid change. Find it now, wherever you get your podcasts.

24:15We've talked a lot about the role of leaders, but peers matter as well. And you talked about the champions. We know that people are much more likely to try new technology or anything new when they're part of a trusted group of people. And we see this with employee resource groups. nearly 90 % of people in ERGs use AI at least once a month, compared to about two-thirds of people who are not part of an ERG. How do you encourage, or speak more about how you encourage that peer-to-peer learning, either formally or informally? I think, again, our worldwide employees have taken initiative in the ERGs.

24:58I know that many Many of them have set up almost those same types of listening sessions or open mic nights to talk about AI and what they're seeing. So I think that because they already had a community, so to speak, and were talking amongst themselves, then it was an opportunity to have built-in trust to say, hey, here's what we're showing here. So I think that that would play out to why we would see those types of metrics of higher engagement there. the AI competency center and the thing I mentioned around champions was essentially trying to do that same or similar pattern, I would call it around those spaces and to enable that at the broader level.

25:36But we would not put them in competition with each other versus let them complement each other. So that's really how we've been approaching that is getting it at the grassroots level, giving people a forum to be able to do those experiments, and then spreading the word to your point. There are varying types of people, if I could put it as it relates to technology adoption. There's the ones that are like, you don't have to ask a question. You've already got it downloaded. You're trying it out. There's your fast followers. And then you've got folks that really, you know, it's not that they're set in their ways, but they really don't want to be wasting their time or that might be their view of trying something if it's not going to work.

26:14And that's fine. We need to work, figure out how to take the strengths of all three of those there Because that third group becomes the engine by which once something is figured out and aligned, it gets adopted and then it gets worked. So, you know, leveraging all three of those and making that kind of essentially for all component of how we do the enablement is key. Maybe one of those events that you can dive a little bit deeper into was, I think it was, if I recall, the great AI unlock. That's what we called the event. It was more or less a town hall, and it was mostly with IT teams. I think it was a lot of software developers.

26:52And one of the pieces of feedback that we got on that event, and I think it was a quote, was there was before the great AI unlock, and now there's after the great AI unlock. And it was because we saw a noticeable uptick in AI usage after that. Describe that event, and what was it about, what was said during that event that led to that uptick? Was it just, you know, clarity of use cases? Was it transparency on how we're using AI? Was it something else? Yeah. So we refer to it as before the great unlock and after. We'll have to come up with an acronym for that because we're in IT. But I think there were a couple of things.

27:28Again, it made something real and focused. So in that particular event, it was focused specifically around how do we use AI for coding and software delivery lifecycle. And it was primarily IT, but we also have technical delivery folks that are in our go-to-market space and consulting, et cetera. So they were a part of these things as well. They help our clients with doing a lot of these things. So it helped in the regard that it created a light and created visibility into all of those early adopters and right after adopters because they got a chance to show off what they were doing. And then it created the light bulb effect for the others to be like, oh, my goodness, this is real.

28:12It cut through the signal to noise. Again, if you read the stuff on the Internet, half of them say this is going to replace your job. The other half are going to say it never works. There's probably some truth in the middle somewhere. And so this event started to show where people were not just talking about what they did, but they were demonstrating what they were doing. And when you demonstrate something, it becomes real. and that was when that became a, and it became an open invitation. We said, here's where you get the tools, here's where you, you know, the licenses and here's where you get the enablement materials.

28:42And we had people even that week that hadn't touched the tools, pick the tools up and turn around the next week and say, here's what I did. One example was in a space that we wouldn't even have expected AI tools to help because it's a legacy technology. Two of our brightest folks in that space use the tool to cut the response time down from 15 minutes to three minutes on a particularly troubling thing. And that was great just for that technical component. But that thing is something that our logistics and our supply chain team members, when they're on the floor, have to wait 15 minutes for that thing to go to function.

29:21So now cutting that by three minutes reduces, you know, just sitting there waiting frustratingly for something to return by a significant factor. And so it improved, it tangibly improved something on the business outcome side as well. And I really see that event as the culmination of a lot of the things that we've already talked about on this episode already, talking about Jim's focus and intent on AI, top-down leadership, openly talking about it in those open mic sessions. All of that really culminated to people wanting to experiment or experiment, innovate with AI. They were able to present it in a clear way at this event.

29:59And, you know, we had a culture of embracing change along the way that helped people give that light bulb moment. So I really think that's a clear example of how a lot of the things that we've already talked about, those values led to a really tangible moment where we had an inflection point. Yeah. Agreed. And I love that you called that out because that's a great actual use case because things can sound good. And then there's like real life, how it works. And that's a great example of peer to peer learning, hearing something. Oh, taking the mystery out of it. I can do that. It encourages people.

30:31As you're saying, it has that wonderful ripple effect. One other quick follow-up on the champions and the open mic night. Those are an open mic night or day, whatever it is. Yeah, right. Who knows what's happening at night at robot. Practical kind of ways to implement that. So if people say, how do I find those champions in my company? How do I know who is using it? And yeah, we'll start with that question. It still kind of goes back to the emphasis on culture. That's what's enabled us to navigate the tumultuous aspects of these things. So in some ways, I'm not saying it's too little too late, but if you're starting the listening sessions now to talk about AI, you're going to have to overcome a little bit of, oh, they just want to talk about AI.

31:18We're fortunate enough that the aspects of the way that our business unit leaders, executive vice presidents work with their organization does entail that aspect of listening and feedback loop, et cetera. So it was natural for them to start asking questions about how are you seeing AI used in your spaces? So that doesn't necessarily help. So maybe if I could, it's not too late, right? If you're open and transparent and saying, hey, we're trying something here and not set it up as such a high bar of we're going to do this and we're going to change the company because of exactly what you say versus this is where we want to go.

31:53Start talking to us about where you're seeing opportunities, where you might have experimented and make it open in that regard. And even if you're on the IT side of it and you don't have the tools yet, use it as, I semi-jokingly call it, an amnesty program. Like, come talk to us about what tools you have or need, because that then informs us on where we need to go in licensing the right things and securing the right things so that we can give you the right components to do those experimentations in a safe and secure way. So the kind of coupling those two together help in creating that mixture that then enables that experimentation and that implementation.

32:31One insightful thing you told me about the AI champions that I thought was interesting and I really hadn't thought about it that way was these aren't people that are simply the best at using AI or they've found. I mean, they are people that are great at using it and they have found breakthroughs, but maybe more importantly, they're people that are getting people on the bandwagon. And that's a very much a people to people. Yes. And getting into relationships and driving trust and things like that. Maybe a little bit more on that. Well, we did pick and we wanted the champions to be in the business because they already know the pain points in the business and they know the frustrations of their team members.

33:09and so it is a logical component of it's less of a technical hurdle versus a let me show you what I did and or once you tell me the problem I'll I'll take it and try and figure it out this way and then we can you know work back and forth with it so when you do have that that higher touch personal connection it creates greater trust it then radiates out it will create more of a removing the hesitancy because they're seeing that it's others of their unit that are doing those things as opposed to, you know, I'm here for my tea and I'm here to help. It's not going to land as well in that regard because we don't always appreciate the nuances of what they're doing.

33:50We try to, but it's better when it's engaged with the employees. And then that affords us, in a way, an ability to concentrate our time and attention in a direct manner through a conduit as opposed to, you know, 50 different tickets coming from across the board versus one conversation that has the depth of where there might be a problem, where there's some sort of error happening or whatnot. And then we can solve those things faster that way too. Yeah. I actually really like that as a very simplified view of AI adoption in the enterprise is ticket versus conversation. Yeah. Anyway, I'll just leave that.

34:27Yeah, tickets, right? Yeah. To close us out, we should mention that Worldwide has been part of a small group of companies who've been working with Great Place to Work to better understand how high-trust workplaces are navigating through AI and the employee experience, which includes frontline workers, the IT-HR partnership, so much more. The through line of it all, as we've talked about today, is trust. What have you learned from the partnership so far and where are you headed next? We went through a session yesterday with that cohort on AI for all and what we're learning. And back to trust, being able to get in a room where we can talk about, first realize that we're not alone.

35:14Everybody's dealing with similar aspects to things. But then through that, you know, there's that whole concept of the answers in the room and being able to converse and trade ideas. And one of the, for me, one of the more insightful ones is we were talking through things, particularly we were honing in on entry level individual contributor roles. And so people, you know, whether it's fresh out of school or you're, you know, an individual contributor versus a manager. And we kind of worked out through that, that one of the things that's interesting about AI, when we're trying to enable you as an individual contributor, is that you are in effect, delegating some aspects of your role or responsibility to AI to do.

35:56And that's something that we typically train new managers when you move into management. It's you learn how do you delegate? How do you give up? You know, because the old adage is always like, I can do it faster if I do it myself. But it's like, but you should give it to somebody else because your time needs to be focused on other things. So long way to get back to we were saying, well, one of the things that we'll probably need to focus on through this is how we change our training and enablement and move some of that delegation training down into individual contributor roles. because they're not necessarily coming out of school learning those things.

36:28You're poised with learning how do I become an expert in something. We still want that, but we now need to teach how you're expected to delegate aspects of that and then verify. Everything about AI still means that you own it. There is the aspect of agency and your responsibility. And the reason we brought you into the company is because of your abilities. We're going to give you AI as a tool and a capability. and then with that delegation, that magnifies your ability to produce and create, but it's still yours too and your responsibility to make sure that the quality is there. So - Mm-hmm. Thank you, Kevin.

37:05You're excellent. Thank you. A wonderful guest. I learned a lot. This might be my longest in-depth conversation with a chief information officer. It's probably my longest in-depth conversation as an IT person, yeah. That's a first. I'll take the blame on that as the co-host. And thank you for being my favorite co-host. Yeah. Well, one of one. We're one for one, both of us. Right, right. Thank you for the opportunity. Thank you. Thank you both. This was great. My pleasure. Thanks for listening. If you enjoyed today's podcast, please leave a five-star rating, write a review, and subscribe so you don't miss an episode.

37:40You can stream this and previous episodes wherever podcasts are available.

37:52Thank you.